Modal Adapter

Modal adapters are lightweight modules designed to enhance pre-trained multimodal models, such as CLIP, without requiring extensive retraining. Current research focuses on developing efficient adapter architectures, often employing attention mechanisms or bottleneck structures, to improve performance on various downstream tasks like image classification, video retrieval, and machine translation by effectively fusing visual and textual information. This approach offers significant advantages in terms of parameter efficiency, reduced training costs, and improved generalization across different datasets and modalities, making it a valuable tool for advancing multimodal learning and its applications.